Monitoring various types of disasters involves diversified requirements, such as the spectral band, resolution, and timeliness. However, at present, different types of observation platforms are separately operated. This isolated resource organization model is insufficient to meet the requirements of various Earth-observation tasks, especially when disasters occur. As a result, it is necessary to construct an Earth-observation network that contains space-air-ground observation resources and makes unified task planning for the included heterogeneous resources, such that the efficiency of the entire observation system is maximized. In this article, an architecture with two planning phases is proposed for the coordinated planning of heterogeneous Earth-observation resources, in which area targets and four types of space-air-ground observation resources [i.e., satellites, unmanned aerial vehicles (UAVs), airships, and ground monitoring vehicles] are considered. The two-phase approach in this architecture includes an area target decomposition phase and a task allocation phase. In the first phase, an area target hierarchical decomposition (ATHD) method is proposed to decompose the area targets into subtasks. In the second phase, a task conflict heuristic allocation (TCHA) method is proposed to allocate the decomposed subtasks to subplanning centers. Extensive experiments on simulated and realistic scenarios are conducted to verify the effectiveness of the proposed ATHD and TCHA methods. The computational results show that the ATHD method substantially improves the efficiency of the coordinated task planning process. Moreover, compared with traditional task allocation methods, the TCHA method could produce high-quality observation plans for the Earth-observation network, as it brings complementary benefits via the comprehensive usage of heterogeneous space-air-ground resources.

Traffic congestion detection poses challenges in spatiotemporal data mining and intelligent transportation research. Existing studies primarily detect traffic congestion based on the speed estimation of traffic flows. Such detection techniques may overlook the formation of traffic congestion in space and time. This research proposes a density-based approach to moving object clustering that extracts the spatiotemporal extents of traffic congestion in three steps. The first step applies a map-matching strategy to project original trajectory points in a planar space onto a road network space and segments the trajectories into consecutive time windows. In the second step, we statistically detect moving clusters with significantly high-density subject to network constrained clustering. The final third step determines moving clusters indicative of traffic congestion through the analysis of both vehicle speed and time spans. Comparative experiments on both simulated trajectories and the real-life taxi trajectories in Wuchang demonstrate that the proposed method outperforms other methods through quantitative evaluations using three indicators, i.e. the precision, recall and F1 value. The proposed approach can illustrate the spatiotemporal regularities of traffic congestion, which can inform dynamic route planning and network design optimization.

Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment, and other related domains. Many unpredictable factors, such as changes in observation task requirements, bad weather, and resource malfunctions, may cause the scheduled observation scheme to become infeasible. In these cases, it is crucial to promptly reformulate high-quality observation schemes while exerting minimal negative effects on the previously scheduled tasks. Accordingly, in this study, a bottom-up distributed coordination framework together with an improved contract net is proposed, aiming to facilitate dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels: 1) neighboring resource coordination; 2) single planning center coordination; and 3) multiple planning center coordination. The observation tasks affected by unpredicted factors are managed along with a bottom-up route from resources to planning centers. This bottom-up distributed coordination framework transfers part of the computing load to various nodes of the observation systems to plan tasks more efficiently and robustly. To support the prompt replanning of multiple tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to quickly obtain a promising replanning scheme. The simulation results demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. In addition, this method can efficiently obtain replanning schemes based on original schemes in dynamic environments.

In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

The detection of anomalous paths from floating vehicle trajectories plays an increasingly important role in dynamic path planning because of its ability to identify fraudulent transport behaviors, unexpected traffic accidents, and traffic restriction areas. Existing studies mostly emphasize the significant deviations based on global shape metrics and cannot manage trajectories with missing segments. To address these problems, this study proposes a new sequential pattern mining based method for detecting anomalous paths (SPDAP) in floating vehicle trajectories. First, discrete trajectory points were transformed into consecutive road segment sequences using map matching and essential empirical information. Focusing on these segment sequences, this method constructs a directed graph and calculates the conditional occurrence probabilities of multi-order sub-paths on the graph. On this basis, we designed a sequential pattern mining algorithm by estimating kernel density distributions to adaptively extract multi-order frequent sub-paths. Finally, these anomalous paths can be identified by matching the operations with frequent paths. Comparative experiments on simulated trajectories demonstrated that SPDAP could accurately, adequately, and adaptively detect anomalous paths. Additionally, using real-life taxi trajectories, we conducted semantic analysis by considering travel time and charges to further derive four patterns from the detected anomalous paths. These patterns are expected to support the identification of fraudulent taxi trips and the discovery of candidate driving routes with a preference for saving time or charge. In the future, we will focus on tracing the intrinsic causes of anomalous paths by introducing multisource information and performing real-time anomalous path detection using adaptive time intervals.